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AOP: 272

Title

A descriptive phrase which references both the Molecular Initiating Event and Adverse Outcome.It should take the form “MIE leading to AO”. For example, “Aromatase inhibition leading to reproductive dysfunction” where Aromatase inhibition is the MIE and reproductive dysfunction the AO. In cases where the MIE is unknown or undefined, the earliest known KE in the chain (i.e., furthest upstream) should be used in lieu of the MIE and it should be made clear that the stated event is a KE and not the MIE.  More help

Deposition of energy leading to lung cancer

Short name
A name that succinctly summarises the information from the title. This name should not exceed 90 characters. More help
Deposition of energy leading to lung cancer

Graphical Representation

A graphical representation of the AOP.This graphic should list all KEs in sequence, including the MIE (if known) and AO, and the pair-wise relationships (links or KERs) between those KEs. More help
Click to download graphical representation template Explore AOP in a Third Party Tool

Authors

The names and affiliations of the individual(s)/organisation(s) that created/developed the AOP. More help

Samantha Sherman1, Zakara Said1, Baki Sadi1, Carole Yauk1,2, Danielle Beaton3, Ruth Wilkins1 Robert Stainforth1, Nadine Adam1,  Vinita Chauhan1,*

Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada

2 Department of Biology, University of Ottawa, Ottawa, ON, Canada

Canadian Nuclear Laboratories, Chalk River, ON, Canada

*Corresponding author: Vinita Chauhan (vinita.chauhan@canada.ca)

Point of Contact

The user responsible for managing the AOP entry in the AOP-KB and controlling write access to the page by defining the contributors as described in the next section.   More help
Vinita Chauhan   (email point of contact)

Contributors

Users with write access to the AOP page.  Entries in this field are controlled by the Point of Contact. More help
  • Vinita Chauhan

Coaches

This field is used to identify coaches who supported the development of the AOP.Each coach selected must be a registered author. More help

Status

Provides users with information concerning how actively the AOP page is being developed, what type of use or input the authors feel comfortable with given the current level of development, and whether it is part of the OECD AOP Development Workplan and has been reviewed and/or endorsed. OECD Status - Tracks the level of review/endorsement the AOP has been subjected to. OECD Project Number - Project number is designated and updated by the OECD. SAAOP Status - Status managed and updated by SAAOP curators. More help
Handbook Version OECD status OECD project
v2.0 WPHA/WNT Endorsed 1.56
This AOP was last modified on October 24, 2023 11:02

Revision dates for related pages

Page Revision Date/Time
Deposition of Energy March 08, 2024 11:49
Increase, DNA strand breaks March 08, 2024 12:05
Inadequate DNA repair March 08, 2024 12:15
Increase, Mutations May 15, 2023 08:47
Increase, Chromosomal aberrations March 08, 2024 12:20
Increase, Cell Proliferation March 08, 2024 12:25
Increase, lung cancer January 10, 2023 19:04
Energy Deposition leads to Increase, DNA strand breaks March 08, 2024 12:44
Energy Deposition leads to Increase, Mutations March 08, 2024 15:24
Energy Deposition leads to Increase, Chromosomal aberrations March 08, 2024 15:26
Increase, DNA strand breaks leads to Inadequate DNA repair March 08, 2024 14:56
Energy Deposition leads to Increase, lung cancer July 05, 2023 15:14
Inadequate DNA repair leads to Increase, Mutations March 08, 2024 15:00
Increase, DNA strand breaks leads to Increase, Mutations January 09, 2023 21:05
Increase, DNA strand breaks leads to Increase, Chromosomal aberrations January 09, 2023 21:05
Increase, Mutations leads to Increase, lung cancer January 10, 2023 19:27
Inadequate DNA repair leads to Increase, Chromosomal aberrations March 08, 2024 15:05
Increase, Mutations leads to Increase, Cell Proliferation March 08, 2024 15:10
Increase, Chromosomal aberrations leads to Increase, lung cancer January 10, 2023 19:29
Increase, Chromosomal aberrations leads to Increase, Cell Proliferation March 08, 2024 15:11
Increase, Cell Proliferation leads to Increase, lung cancer January 10, 2023 19:19
Ionizing Radiation May 07, 2019 12:12

Abstract

A concise and informative summation of the AOP under development that can stand-alone from the AOP page. The aim is to capture the highlights of the AOP and its potential scientific and regulatory relevance. More help

Despite its widespread recognition in chemical toxicology, the adverse outcome pathway (AOP) framework has not been fully explored in the radiation field to guide relevant research and subsequent risk assessment.  Development of a radiation relevant AOP is described here using a case example of lung cancer.  Lung cancer is a major public health problem world-wide, causing the deaths of an estimated 1.5 million people annually; it imposes a major health-care burden. Numerous environmental factors are known contributors including both chemical (e.g.. asbestos, air pollution and arsenic) and radiation stressors (e.g.. radon  gas).  Radon gas is the second leading cause of lung cancer in North America. Evidence suggests that environmental and indoor radon exposure constitutes a significant public health problem. The mechanism of lung cancer development from exposure to radon gas is unclear. Data suggest that cytogenetic damage from radon decay progeny may be an important contributor.  This AOP defines a path to cancer using key events  related to DNA damage response and repair. The molecular initiating event (MIE)  which represents the first chemical interaction with the cell is identified as the  deposition of ionizing energy.  Energy deposited onto a cell can lead to multiple ionization events to targets such as DNA. This energy will break DNA double strands (KE1) and initiate double strand break (DSB) repair machinery.  In higher eukaryotes, this occurs through non-homologous end joining (NHEJ) which is a quick and efficient, but error-prone process (KE2). If DSBs occur in regions of the DNA transcribing critical genes, then mutations (KE3) generated through faulty repair may alter the function of these genes or may cause chromosomal aberrations (KE4), resulting in genomic instability. These events will alter the functions of many gene products and impact cellular pathways such as cell growth, cell cycling, and apoptosis. With these alterations, cell proliferation (KE5) will be promoted by escaping the regulatory control and form hyperplasia in lung epithelial cells, leading eventually to lung cancer (AO) induction and metastasis . The overall weight of evidence for this AOP is strong.   The uncertainties and inconsistencies surrounding this AOP are centred on dose-response relationships associated with dose, dose-rates and radiation quality. The proposed AOP will act as a case example to motivate more researchers in the radiation field to use the AOP framework to effectively exchange knowledge and identify research gaps in the area of low dose risk assessment.

AOP Development Strategy

Context

Used to provide background information for AOP reviewers and users that is considered helpful in understanding the biology underlying the AOP and the motivation for its development.The background should NOT provide an overview of the AOP, its KEs or KERs, which are captured in more detail below. More help

According to the World Cancer Research Fund, lung cancer is a disease that poses a significant healthcare burden world-wide. (https://www.wcrf.org/dietandcancer/cancer-trends/worldwide-cancer-data (https://www.wcrf.org/dietandcancer/cancer-trends/worldwide-cancer-data)). It is the most commonly diagnosed cancer with the highest incidence of occurrence on a global scale (excluding non-melanoma skin cancers). It is a multi-faceted disease exhibiting various genetic lesions and involving the accumulation of multiple molecular abnormalities over time. It is responsible for 1.5 million deaths annually. There is convincing evidence to show that smoking is an important risk modulating factor to lung cancer development.  This risk is increased by age at which one starts, the total number of years  and number of cigarettes smoked/day.  Studies highlight smoking leads to the largest (relative) increases for small cell carcinoma and squamous cell carcinoma and (Sobue et al., 1999 and Janssen-Heijnen et al., 2001). Other risk factors include lack of physical activity, genetic mutations, dietary factors, asbestos, air pollution (de Groot et al., 2012). Although the link between smoking and lung cancer has been well-established, environmental and indoor radiation exposure are also significant contributors. Risk assessment measures for defining acceptable exposure levels of radiation exposure still remain uncertain; including the scientific research to support the justifications. This is partially due to the assumption of a non-threshold and linear model at low doses with no consideration that cellular/tissue effects of low dose radiation exposure remain poorly understood.

Efforts were focused on developing a simple, unidirectional AOP to lung cancer using predominantly available data from radiation studies. Decades of research suggest that energy in the form of ionizing radiation can break DNA molecules. In vitro mutagenicity studies suggest that alterations in genes in the form of mutations, chromosomal aberrations and micronuclei formation may be important for cancer cell differentiation/proliferation and eventually neoplastic transformation (Harris, 1987). The MIE was selected to be “deposition of energy” as it is the initial measurable interaction at the macro-molecular level within an organism that can lead to a perturbation that initiates the AOP. The term accurately defines the initiating phenomena that manifest from any type of radiation insult (e.g. alpha- and beta-particles, photons, neutrons and heavy ions) and is distinguishable from chemical-based initiation events.  Although the “deposition of energy” is itself a physical phenomenon (not biological) it is essential to describe the causal relationship between radiation insults and the stochastic onset of associated downstream biological damage. Historically, this relationship has been empirically observed and reported in the form of dose-response data. In addition, this MIE encapsulates the known varieties of radiation and their differing physical properties while still adhering to the stressor agnostic principles of the AOP framework.

 This AOP has brought together molecular and cellular based research in the radiation realm and defined a modular, simplistic path towards lung cancer. It has used data–rich key events to a classic targeted response onto a cell that is applicable to multiple radiation stressors (e.g. X-rays, gamma rays, alpha particles, beta particles, heavy ions, neutrons) and well supported thorough empirical evidence. The proposed AOP is not the only route to lung cancer it is likely to be one linear path in a network of multiple pathways that may include other critical events.  This hypothetical AOP will be networked to AOP-296, AOP-322, AOP-293, AOP-294 and AOP-303 forming a larger network of KEs related inflammation, apoptosis, and oxidative stress, providing a more complete path to lung cancer. This AOP is also a case example of how existing evidence from radiation stressors can stregthen empirical evidence surrounding key events that may be non-radiation specific and vice versa. By using a radiation centric molecular initiating event (MIE), networks can be developed for multiple adverse outcomes distinct to a radiation response. As different radiation stressors can trigger the MIE, the AOP will have wide applicability.

It is our goal, with the development of this AOP to motivate radiation researchers to use this framework for bringing together research data, exchanging knowledge, identifying priority areas and better co-ordinating research in the low-dose ionizing radiation field.

Strategy

Provides a description of the approaches to the identification, screening and quality assessment of the data relevant to identification of the key events and key event relationships included in the AOP or AOP network.This information is important as a basis to support the objective/envisaged application of the AOP by the regulatory community and to facilitate the reuse of its components.  Suggested content includes a rationale for and description of the scope and focus of the data search and identification strategy/ies including the nature of preliminary scoping and/or expert input, the overall literature screening strategy and more focused literature surveys to identify additional information (including e.g., key search terms, databases and time period searched, any tools used). More help

Summary of the AOP

This section is for information that describes the overall AOP.The information described in section 1 is entered on the upper portion of an AOP page within the AOP-Wiki. This is where some background information may be provided, the structure of the AOP is described, and the KEs and KERs are listed. More help

Events:

Molecular Initiating Events (MIE)
An MIE is a specialised KE that represents the beginning (point of interaction between a prototypical stressor and the biological system) of an AOP. More help
Key Events (KE)
A measurable event within a specific biological level of organisation. More help
Adverse Outcomes (AO)
An AO is a specialized KE that represents the end (an adverse outcome of regulatory significance) of an AOP. More help
Type Event ID Title Short name
MIE 1686 Deposition of Energy Energy Deposition
KE 1635 Increase, DNA strand breaks Increase, DNA strand breaks
KE 155 Inadequate DNA repair Inadequate DNA repair
KE 185 Increase, Mutations Increase, Mutations
KE 1636 Increase, Chromosomal aberrations Increase, Chromosomal aberrations
KE 870 Increase, Cell Proliferation Increase, Cell Proliferation
AO 1556 Increase, lung cancer Increase, lung cancer

Relationships Between Two Key Events (Including MIEs and AOs)

This table summarizes all of the KERs of the AOP and is populated in the AOP-Wiki as KERs are added to the AOP.Each table entry acts as a link to the individual KER description page. More help

Network View

This network graphic is automatically generated based on the information provided in the MIE(s), KEs, AO(s), KERs and Weight of Evidence (WoE) summary tables. The width of the edges representing the KERs is determined by its WoE confidence level, with thicker lines representing higher degrees of confidence. This network view also shows which KEs are shared with other AOPs. More help

Prototypical Stressors

A structured data field that can be used to identify one or more “prototypical” stressors that act through this AOP. Prototypical stressors are stressors for which responses at multiple key events have been well documented. More help

Life Stage Applicability

The life stage for which the AOP is known to be applicable. More help
Life stage Evidence
All life stages High

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected.In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available. More help
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI

Sex Applicability

The sex for which the AOP is known to be applicable. More help
Sex Evidence
Unspecific High

Overall Assessment of the AOP

Addressess the relevant biological domain of applicability (i.e., in terms of taxa, sex, life stage, etc.) and Weight of Evidence (WoE) for the overall AOP as a basis to consider appropriate regulatory application (e.g., priority setting, testing strategies or risk assessment). More help

The present AOP compiles data in the most simplified, modular path to lung cancer from an MIE of deposition of energy. An estimated 1.5 million people worldwide die of lung cancer annually (https://www.wcrf.org) with smoking being the leading cause globally, followed by radon gas. Multiple other environmental factors (e.g., asbestos, air pollution and arsenic) in combination with smoking can increase risk (Hubaux et al., 2012).  Indeed, studies show that the histological lung profile of smokers is very different from non-smokers exposed to high radon levels (Kreuzer et al., 2000). This is in part due to the complexity of the interaction of each stressor with macromolecules within the cell. Therefore, at the different levels of biological organization, it is important to distinguish the mechanisms between lung cancer from smoking and that of radon exposure. Furthermore, studies show that residential radon gas can contribute to lung cancer, however, there remains uncertainty in risk estimates due to the dosimetry of the exposures from the lower doses (Samet et al., 2000 and 2006). The cellular/tissue effects of low dose radiation exposure are still not well understood, and the traditional linear no-threshold model cannot be assumed to accurately estimate risk (Ruhm et al., 2016; Shore et al., 2018). 

Following AOP conventions, KEs were chosen based on the well-accepted understanding of lung cancer from radiation exposures (e.g., radon gas).  Essential key events were identified that are routinely measured by modern and conventional assays. Hallmarks of cancer (i.e., evasion, angiogenesis, etc.) are not included in the AOP, but these could be developed separately and networked in the future. This AOP is the first to use an MIE that is radiation-specific, therefore, this pathway is envisioned to be networked to other health outcomes initiated from radiation exposures. Pathways could be created in parallel including additional KEs leading to non-targeted effects (i.e., immune response/suppression and adaptive response) or non-cancer outcomes.

Although decades of radiobiological and epidemiological research exist in the radiation field, it was a difficult to identify the required elements of the Bradford-Hill criteria (e.g., essentiality, incidence-concordance).  This AOP is best supported by evidence from biological plausibility given the DNA damage response and repair is a well-established and reviewed pathway.  It was noted that studies typically analyzed endpoints at a single time-point, which challenged fully understanding the timeframe of KEs initiation. A few studies used a broad dose-range but did not detail quantitative trends. Additionally, there was limited evidence supporting essentiality, particularly for the latter half of the pathway. This was evident in the KERs of inadequate DNA repair to mutations/chromosomal aberrations (CAs) and mutations/CAs to cellular proliferation, while the non-adjacent KERs (e.g., energy deposition to CAs or energy deposition to mutations) generally were well-supported. Another area of challenge was KERs that were linked directly to the MIE.  For these KERs there were often inconsistencies in findings due to varied exposure parameters related to doses, dose-rates and radiation quality.  Radiation attributes can modulate cancer progression by influencing the type and amount of damage. This aspect can complicate the quantitative understanding of the AOP, although qualitatively the outcomes were observed to be similar. Furthermore, no single study measured all the KEs in this AOP. The lack of studies showing essentiality formed the principal knowledge gap of the AOP. Data supporting dose- and temporal-effects could also be improved across various KERs through more well-conducted animal studies. Uncertainty surrounding modulating factors such as lifestyle, health status and individual radiosensitivity also reduced the strength of the KERs. Additional KEs addressing these factors could be incorporated in parallel as research on these KEs improves.

An assessment of the weight of evidence supporting this AOP determined a strong biological plausibility and a moderate level of empirical evidence. Mathematical simulations and cell-based studies have predominantly provided evidence for dose- and temporal-response relationships. Various factors can influence the initiation of the KEs, including cell type, radiation quality and dose-rate. Therefore, the amount of energy deposited (MIE) that is required to drive the KEs in a pathway leading to cancer remains undefined. This is especially relevant for conflicting concepts of hormesis and hypersensitivity at low doses and low dose-rates. Additionally, because of the nature of the MIE, absolute values of DSBs that are required to surpass the capability of DNA repair mechanisms, resulting in inadequate DNA repair, and the downstream events leading to cancer, remain unknown. The occurrence of tumorigenesis requires more than one 1 hit to the DNA molecule (Loeb et al., 2003), however, there is a low probability that a single ionization event to the DNA molecule will result in the pathway leading to lung cancer. In contrast, at higher doses, cancer formation may not occur, as apoptosis may be induced in damaged cells. Further research involving the development of quantitative and predictive models can strengthen the understanding of the AOP.

The present AOP can be expanded to include KEs related to oxidative stress, signaling pathways and inflammatory mediators. The uncertainties, inconsistencies and knowledge gaps identified in the AOP can inform areas for future research. The AOP demonstrates the framework as a means to compile data, exchange knowledge, and identify priority areas for research in the ionizing radiation field. The current version of this AOP was developed by a team of researchers with backgrounds primarily in AOP development, carcinogenesis, radiobiology, radiation physics and biomolecular epidemiology. However, due to the importance of radiation epidemiology in the international radiological protection system and its underlying assumptions, it seems essential to strengthen the epidemiological aspects of this AOP, a specific area of future improvement. With increased development of radiation relevant AOPs, the AOP framework will have a larger part in supporting the system of radiological protection.

Domain of Applicability

Addressess the relevant biological domain(s) of applicability in terms of sex, life-stage, taxa, and other aspects of biological context. More help

This AOP is relevant to mammals (Eymin & Gazzeri, 2009; Barron et al., 2014; Kurgan et al., 2017). The pathway leading to the development of lung cancer often occurs during adulthood but may be applicable at earlier life stages (Liu et al., 2015) and is independent of sex. In humans, however, genetic abnormalities/mutations suggestive of lung cancer risk seem to be influenced by ethnicity (Lloyd et al., 2013), smoking history (Lim et al., 2009; Sanders & Albitar, 2010; Paik et al., 2012; Lloyd et al., 2013; Cortot et al., 2014; Minina et al., 2017, Cahoon et al., 2017), age (Lloyd et al., 2013), sex (Lim et al., 2009; Cortot et al., 2014) and genotype (Lim et al., 2009; Sanders & Albitar, 2010; Kim et al., 2012; Paik et al., 2012; Leng et al. 2013; Cortot et al., 2014; Minina et al., 2017). Evidence supporting this AOP comes primarily from studies using bacterial DNA (Sutherland et al., 2000; Jorge et al., 2012), human fibroblast cells (Rothkamm & Lo, 2003; Kuhne et al., 2005; Rydberg et al., 2005a), mice (Duan et al., 2008; Zhang & Jasin, 2011), hamsters (Bracalente et al., 2013; Lin et al., 2014), lung cancer cell lines (Sato, Melville B. Vaughan, et al. 2006; Kurgan et al., 2017; Tu et al., 2018), and tissue samples (both with and without lung cancer) Sun et al., 2016; Tu et al., 2018 Warth et al., 2014.

Essentiality of the Key Events

The essentiality of KEs can only be assessed relative to the impact of manipulation of a given KE (e.g., experimentally blocking or exacerbating the event) on the downstream sequence of KEs defined for the AOP. Consequently, evidence supporting essentiality is assembled on the AOP page, rather than on the independent KE pages that are meant to stand-alone as modular units without reference to other KEs in the sequence. The nature of experimental evidence that is relevant to assessing essentiality relates to the impact on downstream KEs and the AO if upstream KEs are prevented or modified. This includes: Direct evidence: directly measured experimental support that blocking or preventing a KE prevents or impacts downstream KEs in the pathway in the expected fashion. Indirect evidence: evidence that modulation or attenuation in the magnitude of impact on a specific KE (increased effect or decreased effect) is associated with corresponding changes (increases or decreases) in the magnitude or frequency of one or more downstream KEs. More help

Support for Essentiality of KEs

Defining Question

Strong

Moderate

Weak

Are downstream KEs and/or the AO prevented if an upstream KE is blocked?

Direct evidence from specifically designed experimental studies illustrating essentiality for at least one of the important KEs

Indirect evidence that sufficient modification of an expected modulating factor attenuates or augments a KE

No or contradictory experimental evidence of the essentiality of any of the KEs

MIE:

Deposition of Energy

Evidence for Essentiality of KE: Strong

This event is difficult to test for essentiality as deposition of energy is a physical stressor and cannot be blocked/decreased using chemicals.  However, there are a number of antioxidant studies demonstrating that treatment with various antioxidants prior to irradiation decreases the number of radiation-induced DSBs (results summarized in a review by Kuefner et al. 2015; Smith et al. 2017).

KE1:

Double-Strand Breaks, Increase

Evidence for Essentiality of KE: Weak

A variety of different studies demonstrate that organisms with compromised DNA repair tend to have an increased incidence of DSBs. Inhibition studies have shown that addition of a DNA repair antagonist results in significant increases in DSBs at 6 and 12 hours post-irradiation (Dong et al. 2017). Similarly, knock-outs/knock-downs of DNA repair proteins also results in persisting DSBs post-irradiation (Rothkamm and Lo 2003; Bracalente et al. 2013; Mcmahon et al. 2016; Dong et al. 2017), with one DNA ligase IV-deficient human cell line showing DSBs 240 - 340 hours after radiation exposure (Mcmahon et al. 2016). Studies by Tatsumi-Miyajima et al., (1993) note the increased rate of supF mutation frequencies following the use of a restriction enzyme, Aval, which induces DSBs in different human fibroblast cell lines transfected with plasmids containing the Aval restriction site.  Kurashige et al. (2017) have demonstrated a decrease in MN frequency following the reduction in DSBs by regulating NAC pre-treatment.

KE2: Inadequate DNA Repair, Increase

Evidence for Essentiality of KE: Strong

There is extensive evidence to demonstrate the essentiality of inadequate repair to downstream events. Studies show that inhibiting DNA repair results in a lack of DNA repair foci post-irradiation (Paull et al. 2000), while cells deficient in ATM (involved in DNA repair) show increased levels of incorrectly rejoined DSBs (Lobrich et al. 2000; Bucher et al. 2021). Similarly, chromosomal aberrations were more frequent after inhibition of various proteins involved in DNA repair (Chernikova et al. 1999; Heterodimer et al. 2002; Wilhelm et al. 2014). Furthermore, when knock-out cell lines (i.e., knock-out of genes involved in DNA repair to increase the incidence of ‘inadequate’ repair)  were examined for genomic abnormalities, increased incidence of chromosomal aberrations were clearly evident (Karanjawala et al. 1999; Cornforth and Bedford 1994; Patel et al. 1998; Simsek and Jasin 2010; Lin et al. 2014; Wilhelm et al. 2014; Mcmahon et al. 2016).  Deficiencies in proteins involved in DNA repair also resulted in altered mutation frequencies relative to wild-type cases (Amundson and Chen 1996; Feldmann et al. 2000; Smith et al. 2003; Wessendorf et al. 2014; Perera et al. 2016). Mutation frequency increased following knocked-down BER-initiating glycosylases (OGG1, NEIL1, MYH, NTH1) in HEK293T human embryonic kidney cells transfected with plasmids that were either positive or negative for 8-oxodG (Suzuki et al., 2010). Moreover, G:C to T:A transversion frequency increased in all analyzed cells. Nallanthighal et al. (2017) demonstrated that inadequate DNA repair impacts MN induction in irradiated Ogg1-deficienct mice (compared to Oggff1+/+ mice).

KE3: Mutations, Increase

Evidence for Essentiality of KE: Strong

Numerous studies show a strong correlation between inadequate DNA repair and mutation incidence, as altered mutation frequencies were evident when there were deficiencies in the proteins involved in DNA repair (Amundson and Chen 1996; Feldmann et al. 2000; Smith et al. 2003; Wessendorf et al. 2014; Perera et al. 2016). Mutations in several different genes, including tumour suppressor gene TP53, have also been shown to increase cell proliferation rates (Hundley et al. 1997; Lang et al. 2004; Ventura et al. 2007; Welcker and Clurman 2008; Duan et al. 2008; Geng et al. 2017; Li and Xiong 2017); mutant or absent TP53 has likewise been implicated in carcinogenesis (Iwakuma and Lozano 2007; Muller et al. 2011; Kim and Lozano 2018). In terms of lung cancer specifically, there are many different studies showing that mutations in TP53, KRAS, and EGFR  are associated with lung carcinogenesis. The conceptual ‘removal’ or ‘blocking’ of these mutations using conditional knock out models, inducible mutation models, and treatment with various antagonizing and agonizing compounds has been observed to reverse or prevent lung tumourigenesis in vivo (Roth et al. 1996; Fisher et al. 2001; Ventura et al. 2007; Iwakuma and Lozano 2007; Jia et al. 2016; Luo et al. 2019, Krasinski 2012). The lung tumourigenesis process was also observed to be expedited by exposure of Gprc5a knock-out mice to a known pulmonary carcinogen; this resulted in more somatic mutations and an increased tumour burden in a much shorter time frame relative to unexposed mice (Fujimoto et al. 2017).   

KE4: Chromosomal Aberrations, Increase

Evidence for Essentiality of KE: Weak

Many studies using a model with inadequate DNA repair (in the form of knock-out cell lines and DNA repair inhibitor studies) demonstrated that chromosomal aberrations were significantly increased when DNA repair was inadequate (Karanjawala et al.; Patel et al. 1998; Deniz Simsek and Jasin 2010; Lin et al. 2014; Wilhelm et al. 2014; Mcmahon et al. 2016, Cornforth 1994). The presence of chromosomal aberrations, particularly gene fusions and translocations, has also been associated with high rates of cellular proliferation (Li et al. 2007; Soda et al. 2007; Guarnerio et al. 2016).There also is support for the essentiality of CAs in the induction of cancer. There were significant increases in CAs (micronuclei, nucleoplasmic bridges and nuclear buds) in peripheral blood lymphocyte cultures after addition of a known pulmonary carcinogen to the cells (Lloyd et al. 2013). Furthermore, introduction of the BCR/ABL translocation in mice resulted in chronic myelogenous leukemia; this was accomplished by lethally irradiating the mice and performing a bone marrow transplant with cells that contained a retrovirus carrying the BCR/ABL translocation (Pear et al. 1998). Furthermore, tumour-inducing A549 cells, which are deficient in TSCL1 due to a loss of heterozygosity at chromosome 11, can induce detectable tumours within 3 weeks of injection; transfection of these A549 cells with genes to correct the TSCL1 deficiency and subsequent injection into mice results in fewer and slower-growing tumours (Kuramochi et al. 2001).

KE5:

Cell Proliferation, Increase

Evidence for Essentiality of KE: Strong

Rates of cellular proliferation have been shown to be increased when there are mutations in key genes associated with cell cycle control, including tumour suppressor gene TP53 (Hundley et al. 1997; Lang et al. 2004; Ventura et al. 2007; Welcker and Clurman 2008; Duan et al. 2008; Geng et al. 2017; Li and Xiong 2017). Cells transformed with various oncogenic mutations that suppressed tumour suppressor genes and enhanced activity of proto-oncogenes also showed increased cellular proliferation rates in the form of higher tumour volumes (Sato et al. 2017). Addition of inhibitors that blocked the pro-proliferative signaling pathway associated with KRAS and EGFR in these oncogenically-transformed cells resulted in lower rates of cellular proliferation (Sato et al. 2017). Similarly, several specific chromosomal gene fusions and translocations have also been associated with increasing the rate of cellular proliferation (Li et al. 2007; Soda et al. 2007; Guarnerio et al. 2016). In cancer cells known to harbor the Philadelphia chromosome (a translocation heavily implicated in the pathogenesis of acute lymphoblastic leukemia), addition of an ERB inhibitor resulted in decreased cellular proliferation rates in the cancer cells (Irwin et al. 2013). In another experiment where human ovarian cancer cells were treated with estrogen, there was an increase in the levels of micronuclei and a corresponding increase in the proliferation rates; addition of an antagonist maintained micronuclei frequencies and cell proliferation rates at control cell levels (Stopper et al. 2003). Cellular proliferation rates were decreased using both in vitro and in vivo carcinogenic models exposed to anti-cancer compounds, which highlights the importance of high cellular proliferation for carcinogenesis (Kassie et al. 2008; Lv et al. 2012; Wanitchakool et al. 2012; Pal et al. 2013; Warin et al. 2014; Tu et al. 2018). Genetic manipulations of genes involved in proliferation also resulted in modified cellular proliferation rates (Lv et al. 2012; Sun et al. 2016).

Evidence Assessment

Addressess the biological plausibility, empirical support, and quantitative understanding from each KER in an AOP. More help

Support for Biological Plausibility of KERs

Defining Question

Strong

Moderate

Weak

Is there a mechanistic relationship between KEup and KEdown consistent with established biological knowledge?

Extensive understanding of the KER based on extensive previous documentation and broad acceptance; Established mechanistic basis

KER is plausible based on analogy to accepted biological relationships, but scientific understanding is  not completely established

There is empirical support for  statistical association between KEs, but the structural or functional relationship between them is not understood

Deposition of Energy (MIE)    --> Double-Strand Breaks, Increase (KE1)

Evidence for Biological Plausibility of KER: Strong

It is well established that ionizing radiation can cause various types of DNA damage including single-strand and double-strand breaks (DSBs) (reviewed in Lomax et al. 2013). In particular, there is evidence for the deposition of energy and a resulting increase in DSBs (Ward 1988; Terato and Ide 2005; Goodhead 2006; Hada and Georgakilas 2008; Asaithamby and Chen 2011; Okayasu 2012; Lomax et al. 2013; Moore et al. 2014; Desouky et al. 2015; Sage and Shikazono 2017; Chadwick 2017; Franken et al., 2012; Frankenberg et al., 1999; Rydberg et al., 2002; Belli et al., 2000). Structural damage from the deposited energy can induce chemical modifications in the form of breaks to the phosphodiester backbone of both strands of the DNA. (Joiner 2009). DSBs are also often formed by indirect interactions with radiation through water molecules. Energy deposited on water molecules by radiation results in the production of reactive oxygen species that can then damage the DNA (Ward 1988; Desouky et al. 2015; Maier et al. 2016).

Deposition of Energy (MIE)    --> Mutations, Increase (KE3)

Evidence for Biological Plausibility of KER: Strong

Many studies across a variety of different models provide evidence that deposition of energy by ionizing radiation results in increased mutation frequencies (Russell et al. 1957; Winegar et al. 1994; Gossen et al. 1995; Suzuki and Hei 1996; Albertini et al. 1997; Dubrova et al. 1998; Dubrova et al. 2000; Canova et al. 2002; Dubrova et al. 2002; Dubrova and Plumb 2002; Masumura et al. 2002; Somers et al. 2004; Burr et al. 2007; Ali et al. 2012; Adewoye et al. 2015; Wilson et al. 2015; Bolsunovsky et al. 2016; Mcmahon et al. 2016; Matuo et al. 2018; Nagashima et al. 2018; Wu et al., 1999; Hei et al., 1997; Nagasawa and Little, 1999; Barnhart and Cox, 1979; Thacker at al., 1982; Zhu et al., 1982; Metting et al., 1992; Schwartz et al., 1991; Chen et al., 1984). Radiation-specific mutational signatures have been identified in a variety of radiation-induced tumours (Sherborne et al. 2015; Behjati et al. 2016), and there is extensive evidence that radiation increases germline mutations in both mice (Dubrova et al. 1998; Dubrova et al. 2000; Dubrova et al. 2002; Somers et al. 2004; Barber et al. 2009; Ali et al. 2012; Adewoye et al. 2015; Wilson et al. 2015) and humans (Dubrova et al. 2002; Dubrova and Plumb 2002).

Deposition of Energy (MIE)    --> Chromosomal Aberrations, Increase (KE4)

Evidence for Biological Plausibility of KER: Strong

Extensive and diverse data from human, animal and in vitro-based studies show ionizing radiation induces a rich variety of chromosomal aberrations (Bauchinger et al. 1994; Schmid et al. 2002; Thomas et al. 2003; Maffei et al. 2004; Tucker et al. 2005a; Tucker et al. 2005b; George et al. 2009; Meenakshi and Mohankumar 2013; Santovito et al. 2013; Arlt et al. 2014; Balajee et al. 2014; Han et al. 2014; Vellingiri et al. 2014; Suto et al. 2015; Adewoye et al. 2015; Cheki et al. 2016; Mcmahon et al. 2016; Morishita et al. 2016; Qian et al. 2016; Basheerudeen et al. 2017; Meenakshi et al. 2017; Abe et al. 2018; Jang et al. 2019; Puig et al., 2016; Barquinero et al., 2004; Curwen et al., 2012; Testa et al., 2018; Franken et al., 2012; Cornforth et al., 2002; Loucas et al., 2013; Nagasawa et al., 1990a; Nagasawa et al., 1990b; Edwards et al., 1980; Themis et al., 2013; Schmid et al., 1996; Mestres et al., 2004; Bilbao et al., 1989; Mill et al., 1996; Brooks, 1975; Tawn and Thierens, 2009; Durante et al., 1992; Hamza and Mohankumar, 2009; Takatsuji and Sasaki, 1984; Moquet et al., 2001; Purrott et al., 1980; duFrain et al., 1979).The mechanism leading from deposition of energy to chromosomal aberrations has been described in several reviews (Smith et al. 2003; Christensen 2014; Sage and Shikazono 2017). Other evidence derives from studies examining the mechanism of copy number variant formation (Arlt et al. 2014) and induction of radiation-induced chromothripsis (Morishita et al. 2016).

Double-Strand Breaks, Increase (KE1) --> Inadequate DNA Repair, Increase (KE2)

Evidence for Biological Plausibility of KER: Strong

 It is well recognized that almost all types of DNA lesions will result in recruitment of repair enzymes and factors to the site of damage, and the pathway involved in the repair of DSBs has been well-documented in a number of reviews, many of which also discuss the error-prone nature of DNA repair (Van Gent et al. 2001; Hoeijmakers 2001a; Khanna and Jackson 2001; Lieber et al. 2003; San Filippo et al. 2008; Lieber et al. 2010; Polo and Jackson 2011; Schipler and Iliakis 2013; Vignard et al. 2013; Betermier et al. 2014; Mehta and Haber 2014; Moore et al. 2014; Rothkamm et al. 2015; Jeggo and Markus 2015; Chang et al. 2017; Lobrich and Jeggo 2017; Sage and Shikazono 2017) Error-prone repair processes are particularly important when DSBs are biologically induced and repaired during V(D)J recombination of developing lymphocytes(Jeggo et al. 1995; Malu et al. 2012) and during meiotic divisions to generate gametes (Murakami and Keeney 2008).

Inadequate DNA Repair, Increase (KE2) --> Mutations, Increase (KE3)

Evidence for Biological Plausibility of KER: Strong

Decades of research have shown that DNA repair pathways are error prone and can cause mutations inherently (such as the error prone NHEJ) (Sishc and Davis 2017). This error-prone repair, however, may be due more to the structure of the DSB ends rather than the repair machinery; more complex breaks require more processing, increasing the likelihood that there will be errors in the DNA sequence upon completion of repair (Betermier et al. 2014; Waters et al. 2014). After being exposed to ionizing radiation, approximately 25 – 50% of double-strand breaks have been shown to be incorrectly repaired (Löbrich et al. 1998; Kuhne et al. 2000; Lobrich et al. 2000).

Inadequate DNA Repair, Increase (KE2) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Biological Plausibility of KER: Strong

DSBs are repaired by non-homologous end joining (NHEJ) and homologous recombination (HR). HR uses a template DNA strand to repair DNA damage, while the more error-prone NHEJ simply religates broken ends back together without the use of a template (van Gent et al. 2001; Hoeijmakers 2001; Jeggo and Markus 2015; Sishc and Davis 2017). Chromosomal aberrations may result if DNA repair is inadequate, meaning that the double-strand breaks are misrepaired or not repaired at all (Bignold, 2009; Danford, 2012; Schipler & Iliakis, 2013). A multitude of different types of chromosomal aberrations can occur, depending on the timing and type of erroneous repair. Examples of chromosomal aberrations include copy number variants, deletions, translocations, inversions, dicentric chromosomes, nucleoplasmic bridges, nuclear buds, micronuclei, centric rings, and acentric fragments. A multitude of publications are available that provide details on how these various chromosomal aberrations are formed in the context of inadequate repair (Ferguson and Alt 2001; Venkitaraman 2002; Povirk 2006; Weinstock et al. 2006; Denis Simsek and Jasin 2010; Lieber et al. 2010; Fenech and Natarajan 2011; Danford 2012; Schipler and Iliakis 2013; Mizukami et al. 2014; Russo et al. 2015; Leibowitz et al. 2015; Rode et al. 2016; Vodicka et al. 2018).  

Mutations, Increase (KE3) -->  Cell Proliferation, Increase (KE5)

Evidence for Biological Plausibility of KER: Strong

It is clearly documented that when enough mutations accumulate in critical genes associated with cell cycling or proliferation, there is potential for uncontrollable cell proliferation to occur, which in some cases leads to carcinogenesis (Bertram 2001; Vogelstein and Kinzler 2004; Panov 2005, Lee and Muller 2010). In fact, one of the hallmarks of cancer is sustained proliferative signalling, and one of the enabling characteristics of this increased proliferation is genomic instability/mutations (Hanahan and Weinberg 2011). Thus mutations are particularly dangerous if they occur in proteins controlling the cell cycle checkpoint for entry into proliferation, such as RB and p53 (Lee and Muller 2010). Activating mutations in proto-oncogenes (Bertram 2001; Vogelstein and Kinzler 2004; Larsen and Minna 2011; Lee and Muller 2010) Lee and Muller 2010, inactivating mutations in tumour suppressor genes (Bertram 2001; Vogelstein and Kinzler 2004; Lee and Muller 2010; Fernandez-Antoran et al. 2019) and inactivating mutations in caretaker/stability genes (Vogelstein and Kinzler 2004; Hanahan and Weinberg 2011) are all associated with abnormal increases the rate of cellular proliferation.

Chromosomal Aberrations, Increase (KE4) --> Cell Proliferation, Increase (KE5)

Evidence for Biological Plausibility of KER: Strong

Chromosomal aberrations are formed when there is inadequate DNA repair (Bignold 2009; Danford 2012; Schipler and Iliakis 2013) or errors during mitosis (Levine and Holland 2018). Chromosomal aberrations have been shown to increase cell proliferation when the aberrations result in the activation of proto-oncogenes (Bertram 2001; Vogelstein and Kinzler 2004), the inactivation of tumour suppressor genes (Bertram 2001; Vogelstein and Kinzler 2004),, or the modification of caretaker/stability genes (Vogelstein and Kinzler 2004). Reviews documenting the contribution of CAs to cellular proliferation and/or cancer development (which implies high rates of cellular proliferation) are available (Mes-Masson and Witte 1987; Bertram 2001; Vogelstein and Kinzler 2004; Ghazavi et al. 2015; Kang et al. 2016). The link between chromosomal instability (CIN), which describes the rate of chromosome gains and losses, and cancer development has also been reviewed (Thompson et al. 2017; Gronroos 2018; Targa and Rancati 2018; Lepage et al. 2019).

Cell Proliferation, Increase (KE5) -->  Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Strong

The means by  which dysregulation of cell proliferation promotes the transformation of normal to carcinogenic cells has been heavily reviewed (Pucci et al. 2000; Bertram 2001; Panov 2005; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011). The cell cycle is essential in controlling cellular proliferation rates, and requires a series of checkpoints to be passed before the cell can fully commit to the process of cell division (Pucci et al. 2000; Bertram 2001; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011). One of the most important checkpoints requires the proper functioning of p53, RB, CDK4 and CDK6. The tumour suppressor p53  plays a particularly important role in stopping the cell cycle when there is DNA damage, and for triggering apoptosis when damage is too severe to be repaired (Bertram 2001; Hanahan and Weinberg 2011; Larsen and Minna 2011). Telomeres also play a role in controlling cell proliferation; when the telomeres become too short to protect the coding DNA, the cell enters into a state of replicative senescence (Bertram 2001; Hanahan and Weinberg 2011). All of these processes play a role in controlling the rate of cellular proliferation within a cell. Cancer may occur when these processes became dysregulated such that cells begin to proliferate at excessively high rates. High rates of proliferation are in fact one of the strongest hallmarks of cancer (Hanahan and Weinberg 2011), and uncontrolled proliferation can be accomplished through sustained proliferative signalling through activation of proto-oncogenes (Bertram 2001; Vogelstein and Kinzler 2004; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011), evading growth suppressors and resisting cell death through suppression of tumour suppressor genes (Bertram 2001; Vogelstein and Kinzler 2004; Eymin and Gazzeri 2009; Hanahan and Weinberg 2011; Larsen and Minna 2011), and overcoming replicative senescence through expression of the telomere-lengthening enzyme telomerase (Bertram 2001; Panov 2005; Hanahan and Weinberg 2011; Larsen and Minna 2011). In lung cancer specifically, commonly activated proto-oncogenes include EGFR, ERBB2, MYC, KRAS, MET, CCND1, CDK4 and BCL2, while commonly inactivated tumour suppressor genes are TP53, RB1, STK11, CDKN2A, FHIT, RASSF1A, and PTEN (Larsen and Minna 2011). Telomerase is also activated in nearly all small cell lung cancer (SCLC) cases, and in over three-quarters of non-small cell lung cancer (NSCLC) cases (Panov 2005; Larsen and Minna 2011).

Double-Strand Breaks, Increase (KE1) --> Mutations, Increase (KE3)

Evidence for Biological Plausibility of KER: Strong

Mechanisms of DNA strand break repair have been extensively studied. It is accepted that non-homologous joining of broken ends can introduce deletions, insertions, or base substitution. In mamalian and yeast cells, both HR and NHEJ can lead to alteration in DNA sequence (Hicks & Haber, 2010; Butning & Nussenzweig, 2013; Byrne et al., 2014; Rodgers & McVey, 2016; Dwivedi & Haver, 2018).

 

Double-Strand Breaks, Increase (KE1) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Biological Plausibility of KER: Strong

DNA strand breaks must occur for chromosomal aberrations to occur. Studies have shown DSBs leading to irreversible damage. The links between DSBs and the role DSB repairs has in preventing chromosomal aberrations is widely discussed, with several reviews available: (van Gent et al., 2001; Ferguson & Alt, 2001; Hoeijmakers, 2001; Iliakis et al., 2004; Povirik, 2006; Weinstock et al., 2006; Natarajan & Palitti, 2008; Lieber et al., 2010; Mehta & Haber, 2014; Ceccaldi et al., 2016; Chang et al., 2017; Sishc & Davis, 2017; Brunet & Jasin, 2018).

Mutations, Increase (KE3) --> Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Moderate

There is strong biological plausibility for the relationship between mutations and lung cancer. Bioinformatics studies have identified unique mutation signature profiles associated with specific types of cancer, including lung adenocarcinoma, lung squamous cell carcinoma and lung small cell carcinoma (Alexandrov et al. 2013; Jia et al. 2014; George et al. 2015). Moreover, mutations/genome instability have been implicated as one of the ‘enabling characteristics’ underlying the hallmarks of cancer (Hanahan and Weinberg 2011). Mutations are thought to promote tumourigenesis by modifying the expression of tumour suppressor genes, proto-oncogenes, and caretaker/stability genes in such a way that promotes cell proliferation and/or suppresses apoptosis (Vogelstein and Kinzler 2004; Panov 2005; Sanders and Albitar 2010; Hanahan and Weinberg 2011; Larsen and Minna 2011).  Commonly mutated genes in lung cancer include TP53, KRAS and EGFR. Mutations in these genes, along with known lung cancer driver mutations, are thought to promote tumourigenesis by stimulating pro-proliferation signalling pathways such as the PI3K-AKT-mTOR pathway and RAS-REF-MEK pathway (Varella-garcia 2009; Sanders and Albitar 2010; Larsen and Minna 2011McCubrey 2006).

Chromosomal Aberrations, Increase (KE4) --> Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Moderate

Chromosomal aberrations, encompassing chromosome-type aberrations, chromatid-type aberrations, micronuclei, and nucleoplasmic bridges, have all been found to be predictive of cancer risk in various human cohorts (Bonassi et al. 2000; Smerhovsky et al. 2002; Hagmar et al. 2004; Norppa et al. 2006; Boffetta et al. 2007; Bonassi et al. 2008; Lloyd et al. 2013; El-zein et al. 2014; Vodenkova et al. 2015; El-zein et al. 2017). Specific categories of CAs, including CNVs (Wrage et al. 2009; Shlien and Malkin 2009; Liu et al. 2013; Mukherjee et al. 2016; Zhang et al. 2016; Ohshima et al. 2017) and gene rearrangements (Bartova et al. 2000; Trask 2002; Sanders and Albitar 2010; Sasaki et al. 2010; Mao et al. 2011), have also been associated with cancer development. Chromosomal aberrations promote tumourigenesis through the alteration of pathways controlling cellular growth and apoptosis (Albertson et al. 2003; Sanders and Albitar 2010). The chromosomal aberration burden may be increased by factors such as aberrant centromeres, telomerase deficiencies paired with poor cell surveillance (Albertson et al. 2003), ionizing radiation (Hei et al. 1994; Weaver et al. 1997; Weaver et al. 2000), and the interplay between non-clonal and clonal CAs (Heng, Bremer, et al. 2006; Heng, Stevens, et al. 2006).

Deposition of Energy (MIE)    --> Lung Cancer, Increase (AO)

Evidence for Biological Plausibility of KER: Strong

The deposition of energy, particularly by radon gas, has been associated heavily with lung cancer (Axelson 1995; Jostes 1996; Beir 1999; Kendall and Smith 2002a; Al-Zoughool and Krewski 2009; Robertson et al. 2013). Deposition of energy that triggers lung carcinogenesis in particular is thought to enter the body through inhalation (Beir 1999; Kendall and Smith 2002b). The inhaled particles are thought to deposit on lung tissue and decay, producing ionizing radiation (Axelson 1995; Beir 1999; Kendall and Smith 2002b; Al-Zoughool and Krewski 2009) that can direct the cell towards carcinogenesis (Axelson 1995; Beir 1999; Robertson et al. 2013). The process of radiation-induced carcinogenesis often follows three steps: initiation, promotion and progression. Initiation refers to the interaction between the radiation and the cell, and results in irreversible genetic changes. Promotion occurs when non-carcinogenic promoter is added to the initiated cells such that it synergistically increases oncogenesis, often through receptor-mediated epigenetic changes. Progression occurs at the point when the cells convert from benign to malignant, and is associated with rapid growth and further accumulation of genomic aberrations (NRC 1990; Pitot 1993).

 

Support for Empirical Evidence of KERs

Defining Question

Strong

Moderate

Weak

Does empirical evidence support that a change in KEup leads to an appropriate change in KEdown? Does KEup occur at lower doses and earlier time points than KEdown and is the incidence of KEup > than that for KEdown?

Inconsistencies?

Multiple studies showing dependent change in both events following exposure to a wide range of specific stressors (Extensive evidence for temporal, dose-response and incidence concordance);  No or few critical data gaps or conflicting data

Demonstrated dependent change in both events following exposure to a small number of specific stressors; Some evidence inconsistent with expected pattern that can be explained by factors such as the experimental design, technical considerations, differences between laboratories, etc.

Limited or no studies reporting dependent change in both events following exposure to a specific stressor (i.e. endpoints never measured in the same study or not at all); And/or significant inconsistencies in empirical support across taxa and species that don’t align with expected pattern for hypothesized AOP

Deposition of Energy (MIE) --> Double-Strand Breaks, Increase (KE1)

Evidence for Empirical Support of KER: Strong

Evidence exists for dose/incidence and temporal concordance between deposition of energy and the resultant formation of DNA double-strand breaks. With increasing ionizing radiation, there is an increase in the frequency of double-strand breaks (Aufderheide et al., 1987; Charlton et al. 1989; Sidjanin, 1993; Reddy et al., 1998; Frankenberg et al., 1999; Rogakou et al. 1999; Belli et al., 2000; Sutherland et al. 2000; Lara et al. 2001; Rydberg et al., 2002; Baumstark-Khan et al., 2003; Rothkamm and Lo 2003; Rogers et al., 2004; Kuhne et al. 2005; Sudprasert et al. 2006; Rube et al. 2008; Beels et al. 2009; Grudzenski et al. 2010; Liao et al., 2011; Franken et al., 2012; Bannik, 2013; Antonelli et al. 2015; Flegal et al., 2015; Markiewicz et al., 2015; Shelke and Das, 2015; Chadwick, 2017; Hamada, 2017; Allen et al., 2018; Cencer et al., 2018; Bains, 2019; Barnard, 2019; Ahmadi et al., 2021; Barnard, 2021). However, dose-rate and radiation quality play a crucial role in determining the degree of DNA damage. Temporally, DSBs have been evident at 3 - 30 minutes post-irradiation (Rogakou et al. 1999; Rothkamm and Lo 2003; Rube et al. 2008; Beels et al. 2009; Kuefner et al. 2009; Grudzenski et al. 2010; Antonelli et al. 2015; Cencer et al., 2018). A significant proportion of the DSBs are resolved within 5 hours of radiation (Kleiman, 1990; Sidjanin, 1993; Rogakou et al. 1999; Rube et al. 2008; Kuefner et al. 2009; Grudzenski et al. 2010; Bannik, 2013; Markiewicz et al., 2015; Shelke and Das 2015; Cencer et al., 2018), with a return to baseline levels by 24 hours in most cases (Aufderheide et al., 1987; Baumstark-Khan et al., 2003; Rothkamm and Lo 2003; Rube et al. 2008; Grudzenski et al. 2010; Bannik et al., 2013; Antonelli et al., 2015; Markiewicz et al., 2015; Russo et al., 2015; Dalke, 2018; Bains, 2019; Barnard, 2019; Ahmadi et al., 2021).

Deposition of Energy (MIE) --> Mutations, Increase (KE3)

Evidence for Empirical Support of KER: Strong

Evidence exists for dose/incidence concordance between deposition of energy by ionizing radiation and a corresponding dose-dependent increase in mutation frequency (Suzuki and Hei 1996; Schmidt and Kiefer 1998; Kraemer et al. 2000; Canova et al. 2002; Bolsunovsky et al. 2016; Mcmahon et al. 2016; Matuo et al. 2018; Nagashima et al. 2018). The linear energy transfer of the radiation (Dubrova and Plumb 2002; Matuo et al. 2018), whether the radiation is chronic or acute (Russell 1958), the radiation type (Schmidt and Kiefer 1998; Masumura 2002), and the tissue being irradiated (Masumura 2002, Gossen 1995) all affect this dose-dependent increase. Temporally, it is well established that an increased incidence of mutations is reported after the deposition of energy by radiation (Winegar 1994, Gossen 1995, Albertini 1997, Dubrova 2002A, Matuo 2018, Canova 2002, Nagashima 2018, Masumura 2002, Russell 1958). Most of these studies, however, span over days and weeks, thus making it difficult to pinpoint exactly when mutations occur. Several studies report the manifestation of mutations within 2 - 3 days of irradiation (Winegar 1994, Masumura 2002, Gossen 1995), with an increased mutation frequency still elevated at 14 (Winegar 1994) and 21 days (Gossen 1995) after radiation exposure. At low doses (<1 Gy) the induction of mutations in cells has been observed for high-LET radiation such as alpha particles (Wu et al., 1999; Hei et al., 1997; Nagasawa and Little, 1999; Barnhart and Cox, 1979; Thacker at al., 1982; Zhu et al., 1982; Metting et al., 1992; Schwartz et al., 1991; Chen et al., 1984; Albertini et al., 1997).

Deposition of Energy (MIE) -->  Chromosomal Aberrations, Increase (KE4)

Evidence for Empirical Support of KER: Strong

Results from many studies indicate dose/incidence and temporal concordance between the deposition of energy and the increased frequency of chromosomal aberrations. There is strong evidence of a dose-dependent increase in a wide range of chromosomal aberrations in response to increasing radiation dose (Schmid 2002, Hande et al. 2003, Thomas 2003, Jang 2019, Abe 2018, Suto 2015, McMahon 2016, Tucker 2005A, Tucker 2005B, Arlt 2014, McMahon 2016, Balajee 2014,George 2009, Maffei 2004, Qian 2015; Puig et al., 2016; Barquinero et al., 2004; Curwen et al., 2012; Testa et al., 2018; Franken et al., 2012; Cornforth et al., 2002; Loucas et al., 2013; Nagasawa et al., 1990a; Nagasawa et al., 1990b; Edwards et al., 1980; Themis et al., 2013; Schmid et al., 1996; Mestres et al., 2004; Bilbao et al., 1989; Mill et al., 1996; Brooks, 1975; Tawn and Thierens, 2009; Durante et al., 1992; Hamza and Mohankumar, 2009; Takatsuji and Sasaki, 1984; Moquet et al., 2001; Purrott et al., 1980; duFrain et al., 1979). Temporally, it is well-established that chromosomal aberrations occur after exposure to radiation (Schmid 2002, Thomas 2003, Balajee 2014, Arlt 2014, George 2009, Suto 2015, Basheerudeen 2017, Tucker 2005A, Tucker 2005B, Abe 2018, Jang 2019), though the exact timing is difficult to pinpoint because most assays take place hours or days after the radiation exposure. One notable study did, however, document the presence of chromosomal aberrations within the first 20 minutes of irradiation, with the frequency increasing sharply until approximately 40 minutes, followed by a plateau (McMahon 2016). By 7 days post-irradiation, the frequencies of most chromosomal aberrations had declined (Tucker 2005A, Tucker 2005B).  It should be noted that chromosomal aberrations induced by ionizing radiation are dependent on dose, dose-rate, and radiation type (Bender et al., 1988; Guerrero-Carbajal et al., 2003; Day et al., 2007, Suzuki 1996, Hande et al. 2003).  

Double-Strand Breaks, Increase (KE1) --> Inadequate DNA Repair, Increase (KE2)

Evidence for Empirical Support of KER: Moderate

Results from many studies indicate dose/incidence and temporal concordance between the frequency of double-strand breaks and the rate of inadequate repair. As DNA damage accumulates in organisms, the incidence of in adequate DNA repair activity (in the form of non-repaired or misrepaired DSBs) also increases (Dikomey 2000, McMahon 2016, Kuhne 2005, Rydberg 2005, Kuhne 2000, Lobrich 2000). DNA damage and its ensuing repair also follow a very similar time course, with both events documented within minutes of a radiation stressor (Pinto 2005, Rothkamm 2003, Asaithambly 2009, Dong 2017, Paull 2000). Uncertainties in this KER include controversy surrounding how error-prone NHEJ truly is (Betemier 2014), differences in responses depending on the level of exposure of a genotoxic substance (Marples 2004), and confounding factors (such as smoking) that affect double-strand break repair fidelity (Scott 2006, Leng 2008).

Inadequate DNA Repair, Increase (KE2) --> Mutations, Increase (KE3)

Evidence for Empirical Support of KER: Moderate

There are several studies that indicate a dose/incidence concordance between inadequate DNA repair and an increased frequency of mutations. Inadequate DNA repair (Ptácek et al. 2001; Mcmahon et al. 2016) and mutation frequencies (Mcmahon et al. 2016) have both been found to increase in a dose-dependent fashion with increasing doses of a radiation stressor. Moreover, specific genomic regions with inadequate DNA repair rates also were found to have increased mutation densities in cancer samples (Perera et al. 2016). Increased mutation frequencies have also been demonstrated in cases where more complex DNA repair is required (Smith et al. 2001). According to the results of this study, evidence of repaired DNA was present prior to the detection of mutations in cases of simple repair, whereas these two events occurred together at a later time point when more complex repair was required (Smith et al. 2001).

Inadequate DNA Repair, Increase (KE2) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Empirical Support of KER:  Moderate

There is little empirical evidence available that directly examines the dose and incidence concordance between DNA repair and CAs within the same study. However, comparison of results from studies that measure either radiation-induced DNA repair or radiation-induced chromosomal aberrations demonstrate that the rate of double-strand break misrepair increases in a dose-dependent fashion with radiation doses between 0 - 80 Gy (Mcmahon et al. 2016), as does the incidence of chromosomal aberrations between doses of 0 - 10 Gy (Thomas et al. 2003; Tucker et al. 2005a; Tucker et al. 2005b; George et al. 2009; Arlt et al. 2014; Balajee et al. 2014; Han et al. 2014; Suto et al. 2015; Mcmahon et al. 2016). Similarly, there is not clear evidence of a temporal concordance between these two events. One study examining DNA repair and micronuclei in irradiated cells pre-treated with a DNA repair inhibitor found that both repair and micronuclei were present at 3 hours and 24 hours post-irradiation. This suggests that there may be temporal concordance (Chernikova et al. 1999). More research, however, is required to establish empirical evidence for this KER.

Mutations, Increase (KE3)    --> Cell Proliferation, Increase (KE5)

Evidence for Empirical Support of KER: Moderate

There is little empirical evidence available that assesses the dose and incidence concordance between mutation frequency and cellular proliferation rates. The correlation between these two events is clear in human epidemiology studies examining the incidence between mutations in specific genes, such as TP53 and BRCA1, and the proliferative status of human tumours (M Jarvis et al. 1998; Schabath et al. 2016). Another study introducing oncogenic mutations into mouse lung epithelial cells demonstrated that the addition of multiple oncogenic mutations to the cells resulted in increased tumour volumes over 40 days (suggestive of cell proliferation); in contrast, cells containing only one of these mutations did not show significant changes in tumour volumes (Sato et al. 2017). Unsurprisingly, there is also little empirical evidence available supporting a temporal concordance between these two events. One review explores the timing between these two events by comparing the somatic mutation theory of cancer and the stem cell division theory of cancer. In the somatic mutation theory, it is suggested that mutations accumulate and result in increased rates of cellular proliferation; the stem cell theory, however, states that high proliferation in stem cells allows the accumulation of mutations (López-lázaro 2018). More research is thus required to establish empirical evidence for this KER.

Chromosomal Aberrations, Increase (KE4)    --> Cell Proliferation, Increase (KE5)

Evidence for Empirical Support of KER: Moderate

There is little empirical evidence available that assesses the dose and incidence concordance between chromosomal aberration frequency and cellular proliferation rates. There are several reviews available that discuss the structure and function of specific human cancer-associated chromosomal aberrations, including BCR-ABL1, ALK fusions, and ETV6-RUNX1 (Mes-Masson and Witte 1987; Ghazavi et al. 2015; Kang et al. 2016). There was no identified evidence supporting dose and incidence concordance. Details from a study where estrogen-responsive cancer cells were treated with estrogen suggested the possibility of a temporal concordance, as both micronuclei levels and proliferation rates were higher in the estrogen-treated cells at 140 and 216 hours post-treatment (Stopper et al. 2003). Overall, however, more empirical evidence is required to support this KER.

Cell Proliferation, Increase (KE5)    --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

There is some empirical evidence of a dose and incidence concordance between cell proliferation and lung carcinogenesis. In a few experiments, rodent lungs exposed to various carcinogens showed increased levels of proliferation and developed squamous metaplasia (Zhong et al. 2005) or full-blown tumours (Kassie et al. 2008). Furthermore, nude mice injected with carcinogenic human NSCLC cells also developed tumours within a few weeks of the injection (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018)(Sun 2016, Pal 2013, Tu 2018, Warin 2014). In terms of temporal concordance between these two events, studies are also limited. Multiple tumour xenograft experiments found that nude mice injected with NSCLC cells develop detectable tumours within two weeks of inoculation, which continued to increase in size over time (Sun 2016, Pal 2013, Tu 2018, Warin 2014). Examination of lung squamous metaplasia after 14 weeks of exposure to high levels of tobacco smoke showed increased cell proliferation markers in comparison to lungs from rats exposed to filtered air (Zhong et al. 2005). Similarly, lung tumours from mice that received carcinogens NNK and BaP orally over 4 weeks were also found to express proliferation markers when examined 27 weeks after the start of the experiment (Kassie et al. 2008).

Double-Strand Breaks, Increase (KE1) --> Mutations, Increase (KE3)

Evidence for Empirical Support of KER: Moderate

There is some evidence demonstrating dose and temporal concordance between the two KEs, both in-viv and in-vitro. These studies used a variety of sources of ionizing radiation as stressors. The types of radiation testing this relationship include X-rays, gamma-rays, alpha particles and heavy ions. Example studies include: (in vitro) Rydberg et al., 2005; Kuhne et al., 2005, 2000; Dikomey et al., 2000; Lobrich et al., 2000, (in vivo) Ptacek et al., 2001. For a discussion of chemical stressors affecting this relationship, see AOP 296.

Double-Strand Breaks, Increase (KE1) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Empirical Support of KER: Moderate

Temporal concordance is clear in both in vitro and in vivo data. However, due to the differences in the methods used to measure strand breaks and chromosomal aberrations, the dose-response of these events often appear to be discordant. Examples of studies relating the links between DSBs and chromosomal aberrations include an in-vitro study of gamma-radiated lymphoblasted cell lines (Trenz et al. 2003) isolated lymphocytes and whole blood samples (Sudpresert et al., 2006) and PL61 cells (Chernikova et al., 1999). Source of high linear energy transfer have also been probed, see Iliakis et al. (2019).

Mutations, Increase (KE3)    --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

Evidence for dose/incidence concordance comes from studies with similar radiological and biological conditions that assessed either the relationship between radiation exposure and mutations, or radiation exposure and cancer. Using various in vitro  models, there was a dose-dependent relationship found for mutation induction and radiation dose (Suzuki and Hei 1996; Weaver et al. 1997; Canova et al. 2002), and for oncogenic transformations and radiation dose (Hei et al. 1994; Miller et al. 1995; Miller et al. 1999). Analyses of lung cancer incidences in radon-exposed rats and uranium miners echo these results (Monchaux et al. 1994; Lubin et al. 1995; Ramkissoon et al. 2018). Likewise, administration of a known pulmonary carcinogen to Gprc5a knock-out mice resulted in an increased rate of tumourigenesis and increased mutation accumulation relative to saline-treated mice (Fujimoto et al. 2017). Increasing the number of mutations in vitro  and  in vivo resulted in cells becoming increasingly oncogenic (Sato, Melville B Vaughan, et al. 2006; Sasai et al. 2011) and mice sporting a faster rate of lung tumourigenesis (Fisher et al. 2001; Kasinski and Slack 2012), respectively. In terms of temporal concordance, there is some evidence from separate studies indicating that mutations precede tumourigenesis (Hei et al. 1994; Lubin et al. 1995; Hei et al. 1997; Miller et al. 1999; Fujimoto et al. 2017) , particulary in Cre-inducible models where Cre expression must be induced for the mutations to be expressed (Fisher et al. 2001; Kasinski and Slack 2012).

Chromosomal Aberrations, Increase (KE4)    --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

Evidence for dose/incidence concordance comes from epidemiological studies of radon-exposed uranium miners that found there was an increased CA load with increasing radon exposure (Smerhovsky et al. 2002), and an increased risk of lung cancer with increased cumulative radon exposure (Tirmarchel et al. 1993; Smerhovsky et al. 2002; Vacquier et al. 2008; Walsh et al. 2010). In vivo and in vitro studies have also shown a dose-dependent increase in CAs in lung and non-lung cell lines (Nagasawa et al. 1990; Deshpande et al. 1996; Yamada et al. 2002; Stevens et al. 2014) and lung cells of rodents with increasing radiation dose (A.L. Brooks et al. 1995; Khan et al. 1995; Werner et al. 2017), and a dose-dependent increase in oncogenic transformation in non-lung cells lines (Robertson et al. 1983; Miller et al. 1996)  and in rodent lung tumours with increasing radiation dose (Monchaux et al. 1994; Yamada et al. 2017) Furthermore, there are several published reviews that provide evidence for associations between radon exposure and the appearance of CAs, and radon exposure and the incidence of lung cancer (Jostes 1996; Al-Zoughool and Krewski 2009; Robertson et al. 2013). Likewise, more CAs were found to accumulate in larger tumours (To et al. 2011) and in increasingly more oncogenic lung tissue lesions (Thibervile et al. 1995; Wistuba et al. 1999). There is also evidence for temporal concordance as, the time gap between radiation exposure and the increased incidence of CAs is hours to days (Nagasawa et al. 1990; A.A.L. Brooks et al. 1995; Deshpande et al. 1996; Yamada et al. 2002; Stevens et al. 2014; Werner et al. 2017), while the time gap between radiation exposure and the development of oncogenic transformations or lung tumours is weeks, months or years (Robertson et al. 1983; Tirmarchel et al. 1993; Miller et al. 1996; Pear et al. 1998; Kuramochi et al. 2001; Yamada et al. 2017).

Deposition of Energy (MIE)  --> Lung Cancer, Increase (AO)

Evidence for Empirical Support of KER: Moderate

There is strong evidence of the relationship between radiation exposure and lung carcinogenesis in human epidemiological studies that assess radon exposure and the risk of lung cancer. Results from numerous studies assessing indoor residential radon exposure and outdoor radon exposure in miners suggest that there is a positive association between cumulative radon exposure and lung cancer risk (Lubin et al. 1995; Hazelton et al. 2001; Darby et al. 2005; Krewski et al. 2005; Krewski et al. 2006; TAl-Zoughool and Krewski 2009; Torres-Durán et al. 2014; Kreuzer et al. 2015; Sheen et al. 2016; Rodríguez-Martínez et al. 2018; Ramkissoon et al. 2018; Rage et al. 2020). Several in vitro studies showed that cells could be induced to obtain oncogenic characteristics through radiation exposure (Hei et al. 1994; Miller et al. 1995). Likewise, irradiation of rats at radon levels comparable to those experienced by uranium miners resulted in a dose-dependent increase in lung carcinoma incidence (Monchaux et al. 1994). There is also evidence of temporal concordance, as the oncogenic characteristics of the radon-exposed cells were not evident until weeks after the irradiation (Hei et al. 1994; Miller et al. 1995), while tumours took months to years to grow (Hei et al. 1994; Monchaux et al. 1994). In humans, the risk of lung cancer was also found to increase with increasing time since exposure (Hazelton et al. 2001) at a mean time of 15 years (Aßenmacher et al. 2019) and with longer periods of exposure (Lubin et al.1995).

Known Modulating Factors

Modulating factors (MFs) may alter the shape of the response-response function that describes the quantitative relationship between two KES, thus having an impact on the progression of the pathway or the severity of the AO.The evidence supporting the influence of various modulating factors is assembled within the individual KERs. More help
Modulating Factor (MF) Influence or Outcome KER(s) involved
     

Quantitative Understanding

Optional field to provide quantitative weight of evidence descriptors.  More help

There is strong biological plausibility and empirical evidence to suggest a qualitative link between the deposition of energy on DNA to the final adverse outcome of lung cancer. This evidence has been derived predominately from laboratory studies and through mathematical simulations using cell-based models. The studies show both dose and temporal-response relationships for a select KEs. The quantitative thresholds to initiate each of the KEs are not definitive and have been shown to vary with factors such as the cell type, dose-rate of exposure and radiation quality. Thus, an absolute amount of deposited energy (MIE) to drive a key event forward to a path of cancer is not yet definable. This is particularly relevant to low doses and low dose-rates of radiation exposure where the biology is interplayed with conflicting concepts of hormesis, hypersensitivity and the linear no threshold theory. Furthermore due to the stochastic nature of the MIE, it remains difficult to identify specific threshold values of DSBs needed to overwhelm the DNA repair machinery to cause “inadequate” DNA repair leading to downstream genetic abnormalities and eventually cancer. With a radiation stressor, a single hit to the DNA molecule could drive a path forward to lung cancer; however this is with low probability.  Empirical modeling of cancer probability vs. mean radiation dose and time to lethality, does provide a good visualization of the effective thresholds (Raabe 2011). However, in general there is considerable uncertainty surrounding the connection of biologically contingent observations and stochastic energy deposition.

Raabe OG. Toward improved ionizing radiation safety standards. Health Phys 101: 84–93; 2011.

Support for Quantitative Understanding of KERs

Defining Question

Strong

Moderate

Weak

What is the extent to which a change in KEdown can be predicted from KEup? What is the precision with which uncertainty in the prediction of KEdown can be quantified? What is the extent to which known modulating factors or feedback mechanisms can be accounted for? What is the extent to which the relationships can be reliably generalized across the applicability domain of the KER?

Change in KEdown can be precisely predicted based on a relevant measure of KEup; Uncertainty in the quantitative prediction can be precisely estimated from the variability in the relevant KEup measure; Known modulating factors and feedback/ feedforward mechanisms are accounted for in the quantitative description; Evidence that the quantitative relationship between the KEs generalizes across the relevant applicability domain of the KER

Change in KEdown can be precisely predicted based on relevant measure of KEup; Uncertainty in the quantitative prediction is influenced by factors other than the variability in the relevant KEup measure; Quantitative description does not account for all known modulating factors and/or known feedback/ feedforward mechanisms; Quantitative relationship has only been demonstrated for a subset of the overall applicability domain of the KER

Only a qualitative or semi-quantitative prediction of the change in KEdown can be determined from a measure of KEup; Known modulating factors and feedback/ feedforward mechanisms are not accounted for; Quantitative relationship has only been demonstrated for a narrow subset of the overall applicability domain of the KER

Deposition of Energy (MIE) --> Double-Strand Breaks, Increase (KE1)

Evidence for Quantitative Understanding of KER: Strong

The vast majority of studies examining energy deposition and incidence of DSBs suggest a positive, linear relationship between these two events (Aufderheide et al., 1987; Sidjanin, 1993; Frankenberg et al., 1999; Sutherland et al. 2000; Lara et al. 2001; Baumstark-Khan et al., 2003; Rothkamm and Lo 2003; Kuhne et al. 2005; Rube et al. 2008; Grudzenski et al. 2010; Bannik et al., 2013; Shelke and Das 2015; Antonelli et al. 2015; Dalke, 2018). Predicting the exact number of DSBs from the deposition of energy, however, appears to be highly dependent on the biological model, the type of radiation and the radiation dose range, as evidenced by the differing calculated DSB rates across studies (Charlton et al. 1989; Rogakou et al. 1999; Sutherland et al. 2000; Lara et al. 2001; Rothkamm and Lo 2003; Kuhne et al. 2005; Rube et al. 2008; Grudzenski et al. 2010; Antonelli et al. 2015) .

Deposition of Energy (MIE) --> Mutations, Increase (KE3)

Evidence for Quantitative Understanding of KER: Strong

Most studies indicate a positive, linear relationship between the radiation dose and the mutation frequency (Russell et al. 1957; Albertini et al. 1997; Canova et al. 2002; Dubrova et al. 2002; Nagashima et al. 2018). In order to predict the number of mutations induced by a particular dose of radiation, parameters such as the type of radiation, the radiation’s LET, and the type of model system being used should be taken into account (Albertini et al. 1997; Dubrova et al. 2002; Matuo et al. 2018; Nagashima et al. 2018). Predicting the mutation frequency at particular time-points, however, would be very difficult owing to our limited time scale knowledge.

Deposition of Energy (MIE) -->  Chromosomal Aberrations, Increase (KE4)

Evidence for Quantitative Understanding of KER: Strong

Most studies indicate a positive, linear-quadratic relationship between the deposition of energy by ionizing radiation and the frequency of chromosomal aberrations (Schmid et al. 2002; Suto et al. 2015; Abe et al. 2018; Jang et al. 2019). Equations describing this relationship were given in a number of studies (Schmid et al. 2002; George et al. 2009; Suto et al. 2015; Abe et al. 2018; Jang et al. 2019), with validation of the dose-response curve performed in one particular case (Suto et al. 2015). In terms of time scale predictions, this may still be difficult owing to the often-lengthy cell cultures required to assess chromosomal aberrations post-irradiation. For translocations in particular, however, one study defined a linear relationship between time and translocation frequency at lower radiation doses (0 - 0.5 Gy) and a linear quadratic relationship at higher doses (0.5 - 4 Gy) (Tucker et al. 2005b).

Double-Strand Breaks, Increase (KE1)  --> Inadequate DNA Repair, Increase (KE2)

Evidence for Quantitative Understanding of KER: Moderate

According to studies examining DSBs and DNA repair after exposure to a radiation stressor, there was a positive linear relationship between DSBs and radiation dose (Lobrich et al. 2000; Rothkamm and Lo 2003; Kuhne et al. 2005; Asaithamby and Chen 2009), and a linear-quadratic relationship between the number of misrejoined DSBs and radiation dose (Kuhne et al. 2005) which varied according to LET (Rydberg et al. 2005b) and dose-rate (Dikomey and Brammer 2000) of the radiation. Overall, 1 Gy of radiation may induce between 35 and 70 DSBs (Dubrova et al. 2002; Rothkamm and Lo 2003), with 10 - 15% being misrepaired at 10 Gy (Mcmahon et al. 2016) and 50 - 60% being misrepaired at 80 Gy (Lobrich et al. 2000; Mcmahon et al. 2016). Twenty-four hours after radiation exposure the frequency of misrepair appeared to remain relatively constant around 80%, a rate that was maintained for the next ten days of monitoring (Kuhne et al. 2000).

Inadequate DNA Repair, Increase (KE2) --> Mutations, Increase (KE3)

Evidence for Quantitative Understanding of KER: Moderate

Positive relationships have been reported between radiation stressor and inadequate DNA repair, radiation stressor and mutation frequency (Mcmahon et al. 2016), and inadequate DNA repair and mutation frequency (Perera et al. 2016). It has been found that 10 - 15% of DSBs are misrepaired at 10 Gy (Mcmahon et al. 2016) and 50 - 60% at 80 Gy (Lobrich et al. 2000; Mcmahon et al. 2016), with mutation rates varying from 0.1 - 0.2 mutation per 104 cells at 1 Gy and 0.4 - 1.5 mutation per 104 cells at 6 Gy (Mcmahon et al. 2016).

Inadequate DNA Repair, Increase (KE2) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Quantitative Understanding of KER: Weak

A direct quantitative understanding of the relationship between inadequate DNA repair and chromosomal aberrations has not been established. However, some data has been generated using studies from radiation stressor studies. At a radiation dose of 10 Gy, the rate of DSB misrepair was found to be approximately 10 - 15% (Lobrich et al. 2000); this rate increased to 50 - 60% at a radiation exposure of 80 Gy (Kuhne et al. 2000; Lobrich et al. 2000; Mcmahon et al. 2016). It is not known, however, how this rate of misrepair relates to chromosomal aberration frequency. Results from one study using a DNA repair inhibitor suggested that as adequate DNA repair declines, the chromosomal aberration frequency increases (Chernikova et al. 1999).  The time scale between inadequate repair and chromosomal aberration frequency has also not been well established.

Mutations, Increase (KE3)    --> Cell Proliferation, Increase (KE5)

Evidence for Quantitative Understanding of KER: Weak

 Quantitative understanding of the relationship between these two events has not been well established. There are, however, some studies that have examined how cellular proliferation changes over time in the presence of mutations. In cells harbouring mutations in critical genes, higher proliferation rates were evident by the fourth day in culture (Lang et al. 2004; Li and Xiong 2017) and higher rates of population doublings were evident by passage 7 (Li and Xiong 2017) relative to wild-type cells. DNA synthesis (which could be indicative of cellular proliferation) was higher in p53-/- cells than in wild-type cells for the first 6 days of culture, and increased to drastically higher levels in the knock-out cells until the end of the experiment at day 10 (Lang et al. 2004). In vivo, mice injected with oncogenically-transformed cells containing multiple mutations had detectable tumour growth by 10 - 12 days post-inoculation. These volumes continued increasing over the 40-day experiment (Sato et al. 2017).  

Chromosomal Aberrations, Increase (KE4)    --> Cell Proliferation, Increase (KE5)

Evidence for Quantitative Understanding of KER: Weak

Quantitative understanding of the relationship between these two events has not been well established. . Although studies that directly assessed the time scale between chromosomal aberrations and cell proliferation rates were not identified, differences in cellular proliferation rates for cells with different CA-related manipulations or treatments were evident within the first 3 days of culture (Stopper et al. 2003; Li et al. 2007; Soda et al. 2007; Irwin et al. 2013; Guarnerio et al. 2016).

Cell Proliferation, Increase (KE5)    --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Weak

Quantitative understanding of the relationship between these two events has not been well established. Human non-carcinogenic cells are thought to undergo 50 – 70 cell divisions before the telomeres can no longer support cell division (Panov 2005); this number would presumably be higher in cancer cells, but  quantitative data was not able to be identified. There are some studies available, however, that provide some details regarding the timing between these two events. In vitro experiments using lung cancer cell lines demonstrated that expression levels of key proteins involved in the regulation of the cell cycle and/or proliferation were modified by chemical inhibitors within the first 48 hours of treatment (Lv et al. 2012; Wanitchakool et al. 2012; Pal et al. 2013; Sun et al. 2016). In vivo studies using xenograft nude mice found that tumours were detected within two weeks of NSCLC-cell inoculation, and continued to grow over the experimental period (Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018). Differences in tumour growth rates between mice treated with an anti-cancer drug and those left untreated were also evident within 13 - 27 days (Pal et al. 2013; Sun et al. 2016; Tu et al. 2018), with significant differences in cell proliferation markers and tumour numbers or sizes at time of harvest (22 days - 27 weeks) (Kassie et al. 2008; Pal et al. 2013; Warin et al. 2014; Sun et al. 2016; Tu et al. 2018).

Double-Strand Breaks, Increase (KE1) --> Mutations, Increase (KE3)

Evidence for Quantitative Understanding of KER: Weak

There is overall limited quantitiative understanding of the relationship between DSBs and increased mutation rates. McMahon et al., 2016 compiled data from multiple studies spanning different human and mouse cell lines to model the IR dose-dependent increase in mutation rate. However, further quantitiative studies into this relationship are required to provide a better quantitiative understanding.

Double-Strand Breaks, Increase (KE1) --> Chromosomal Aberrations, Increase (KE4)

Evidence for Quantitative Understanding of KER: Weak

Similarly to the non-adjacent relationship above (KE1 -> KE4), there is overall limited quantitiative understanding of the relationship between DSBs and increased rates of chromosomal aberrations. McMahon et al., 2016 compiled data from multiple studies spanning different human and mouse cell lines to model the IR dose-dependent increase in the rate of chromosomal aberrations. However, further quantitiative studies into this relationship are required to provide a better quantitiative understanding.

Mutations, Increase (KE3)    --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Weak

Finding studies addressing the quantitative relationship between mutations and cancer directly was particularly challenging. However, many studies indicated that there was a positive, dose-dependent increase in mutations with increasing radiation dose (Suzuki and Hei 1996; Canova et al. 2002). A similar positive, dose-dependent relationship was found for the oncogenic transformations in cell and the radiation dose (Miller et al. 1995), and the incidence of lung cancer in rats and their cumulative radon exposure (Monchaux et al. 1994). Epidemiological studies examining lung cancer in radon-exposed uranium miners found a positive, linear relationship between lung cancer and cumulative radon exposure (Lubin et al. 1995; Ramkissoon et al. 2018). In terms of time-scale, mutations were evident in 2 weeks following irradiation (Hei et al. 1997), whereas oncogenic transformations took 7 weeks to develop following radiation exposure (Miller et al. 1999). In vivo models with injected tumour cells, inherent mutations, exposure to carcinogens, or Cre-induced mutations showed tumour growth months after exposure to the tumour-inducing insult (Hei et al. 1994; Fisher et al. 2001; Kasinski and Slack 2012; Fujimoto et al. 2017).

Chromosomal Aberrations, Increase (KE4)    --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Moderate

There is evidence of a positive, linear relationship between radiation dose and CAs (Nagasawa et al. 1990; A.L. Brooks et al. 1995; Khan et al. 1995; Yamada et al. 2002; Stevens et al. 2014), radiation dose and oncogenic transformations (Miller et al. 1996), as well as radon exposure and the risk of lung cancer mortality (Tirmarchel et al. 1993; Walsh et al. 2010). The latter relationship was found to be exponentially modified, however, by factors such as the age at median exposure, the time since median exposure, and the radon exposure rate (Walsh et al. 2010). Equations defining these relationships were derived in a number of different studies (Tirmarchel et al. 1993; A.L. Brooks et al. 1995; Khan et al. 1995; Miller et al. 1996; Girard et al. 2000; Yamada et al. 2002; Walsh et al. 2010; Stevens et al. 2014). In terms of time scale, micronuclei were documented in cells of the rodent lung as early as 0.2 days (Khan et al. 1995), and were found to persist for days to weeks (Khan et al. 1995; Deshpande et al. 1996; Werner et al. 2017). Oncogenic transformations, on the other hand, took weeks to develop (Robertson et al. 1983; Miller et al. 1996), while lung tumours took months or years to develop following radiation exposure (Tirmarchel et al. 1993; Yamada et al. 2017). Delivery of an agent carrying a cancer-related CA resulted in tumour growth within 21 - 31 days of its injection into mice (Pear et al. 1998; Kuramochi et al. 2001).

Deposition of Energy (MIE) --> Lung Cancer, Increase (AO)

Evidence for Quantitative Understanding of KER: Moderate

Quantitative understanding has been well-established for this KER. According to current Canadian guidelines developed by Health Canada, annual residential radon levels should not exceed 200 Bq/m3. Similarly, the WHO recommends that the national annual residential radon levels not exceed 100 Bq/m3 where possible; if there are geographic or national constraints that make this target unachievable, the national standard should not be higher than 300 Bq/m3 (World Health Organization - Radon Guide 2009). Positive relationships between radon exposure and lung cancer have been established using in vitro models (Miller 1995), in vivo models(Monchaux et al. 1994) and results from human epidemiological studies (Lubin et al. 1995; Hazelton et al. 2001; Darby et al. 2005; Krewski et al. 2005; Krewski et al. 2006; Rodríguez-Martínez et al. 2018; Ramkissoon et al. 2018). Unsurprisingly, oncogenic transformation in cells were found weeks after radiation exposure (Miller et al. 1995), sizable tumours developed months after irradiation in mice (Hei et al. 1994) and lung cancer was found years after exposure in humans (Lubin et al. 1995; Darby et al. 2005; Torres-Durán et al. 2014; Rodríguez-Martínez et al. 2018; Ramkissoon et al. 2018).

Quantification of AOP KERs

The development of quantitative AOPs (qAOPs) has been demonstrated in other fields such as chemical toxicology (Zgheib et al., 2019) and similar objectives are warranted for AOPs with ionizing radiation stressors. The quantification of an AOP can help expedite the development of an AOP by reducing the original long-form and qualitative nature of an AOP to tables and graphs that summarize particular features e.g. dose ranges considered, radiation types included etc. Quantification is achieved by extracting numerical information from the underlying supporting evidence of KERs. The quantification of four key event relationships (KERs) from this AOP has been completed. The KERs which have been quantified are as follows:

  1. Energy deposition leads to Increase, DNA strand breaks (Ad-KER1)

  2. Energy deposition leads to Increase, mutations (NAd-KER1)

  3. Energy deposition leads to Increase, Chromosomal aberrations (NAd-KER2)

  4. Energy deposition leads to Increase, lung cancer (NAd-KER7)

For each of the KERs listed above, all relevant publications from those used to support the AOP were considered for quantification. In some cases, the measure of dose-response featured in one publication could not be reconciled with the measure adopted by another. For example, in the study of energy deposition leading to an increase in DNA strand breaks, Sudprasert et al. (2006) use a measure of olive moment from the Comet assay technique, whereas Sutherland et al. (2000) measure the relative site frequency compared to a benchmark instance of DNA damage. Due to variations such as these, not all studies that contribute qualitatively to supporting the weight of evidence of a given KER is eligible for quantification. In the case of the four KERs considered above, the most common measure of response across studies was adopted ensure the largest data sample possible. These response measured were as follows (in same order for each KER listed above):

  1. Ad-KER1 - DNA DSBs / cell

  2. NAd-KER1 - Mutations / 106 cells

  3. NAd-KER2 - Chromosomal aberrations / 100 cells

  4. NAd-KER7 - Relative risk (RR) of lung cancer

The quantification of these four key event relationships (KERs) from this AOP has been completed as detailed in Stainforth et al., 2021. The process of quantification first involves digitizing data from publications. Results provided from tables were used directly. For figures (e.g. scatter or bar-charts) information was obtained by using the WebPlotDigitizer-4.2 authored by Rohatgi (2019). Full information of all quantified studies and respective references can be found in Tables 1-7, here.

The two dominant radiation types featured in the AOP are from photon and alpha-particle sources, see Table 1 below. Upstream KERs describing Ad-KER1, NAd-KER1 and NAd-KER2 are respectively composed of datasets with 298, 176 and 629 data points with 59%, 39% and 57% from photon sources and 35%, 52% and 42% from alpha-particle sources. The AO (NAd-KER7) is 100% characterized by radon (alpha-particle emitter) with a total of 33 data points.

A graphical representation of the four quantified KERs is shown in Figure 1. This AOP is best documented for alpha-particles but could potentially support further data relevant to lung cancer incidence from photon radiation sources. The scope of the AOP could be extended with additional data from proton and heavy ion sources. This would encapsulate research areas such as space-travel where galactic radiation is predominantly composed of protons, and to a lesser extent, heavy ions (Chancellor et al., 2014). Overall, Figure 1 and Table 1 demonstrate how reviewing supporting empirical evidence through a quantitative lens reduces the description of an AOP to tables and graphs that can be used to identify inconsistencies and potential missing information across KERs and radiation types.  

 

Radiation quality

Photons

Protons

Alpha-particles

Heavy ions

KER

Values of dose, response, time and dose rate quoted as [minimum, maximum, average]

 

Dose [Gy]

Ad-KER1

[1.2x103, 80, 7.9]

[0.5, 0.5, 0.5]

[0.1, 713, 203]

[0.5, -, -]

NAd-KER1

[1.7x10-5, 14, 2.4]

[1.24, 3.74, 2.5]

[3.4x10-5, 2.4, 0.6]

[10, 20, 11.8]

NAd-KER2

[6.3x10-4, 10, 1.8]

N/A

[4.3x10-4, 6.9, 0.7]

[0.15, 1.5, 0.7]

NAd-KER7

[4.8x10-2, 2.63, 0.9]

N/A

[7.89x10-3, 10.1, 0.63]

N/A

 

Response measures [DNA DSBs / cell (Ad-KER1), Mutant frequency / 106 cells (NAd-KER1), CAs / 100 cells (NAd-KER2), Increase in lung cancer RR [%] (NAd-KER7)]

Ad-KER1

[5x10-3, 2.8x103, 244]

[0.34, 10.1, 5.3]

[1.3, 3x104, 9.31x103]

[0.4, 8.8, 4.3]

NAd-KER1

[0.3, 1.9x103, 148]

N/A

[1.7, 3.8x103, 279]

[0.4, 19.4, 4]

NAd-KER2

[0.01, 584, 44.8]

N/A

[0.08, 314, 34.9]

[13.2, 138, 5.7]

NAd-KER7

[2.7, 166, 64.4]

N/A

[-17.9, 942, 84.4]

N/A

 

Time [hours (Ad-KER1), days (Ad-KER1, NAd-KER2), years (NAd-KER7)]

Ad-KER1

[0.02, 72, 10.6]

[0.03, 24, 6.5]

[0.02, 24, 0.5]

[0.25, 24, 6.5]

NAd-KER1

[6.9x10-4, 67, 5.3]

[6.9x10-4, -, -]

[6.94x10-4, 6, 1.4]

[6.94x10-4, 2, 0.1]

NAd-KER2

[6.9x10-4, 56, 1.2]

N/A

[6.94x10-4, 362, 23.6]

[6.94x10-4, -, -]

NAd-KER7

[40, -, -]

N/A

[5.7, 39.0, 18.5]

N/A

 

Dose rate [Gy/min]

Ad-KER1

[0.03, 2, 0.9]

N/A

[0.08, 100, 51.5]

N/A

NAd-KER1

[1.1x10-6, 1.2, 0.5]

N/A

[2x10-3, 3.6, 1.3]

[1, 5, 4.8]

NAd-KER2

[1.7x10-3, 5.9, 0.9]

N/A

[5.3x10-6, 2.3, 0.4]

[0.5, -, -]

NAd-KER7

[2.27x10-9, 1.25x10-7, 4.15x10-8]

N/A

[7.7x10-10, 3.4x10-6, 1.8x10-7]

N/A

 

% data points for KER dataset with valid dose and response values (number of data points)

Ad-KER1

59 (177)

3 (8)

35 (105)

3 (8)

NAd-KER1

40 (75)

3 (6)

48 (91)

9 (17)

NAd-KER2

56 (344)

0 (0)

43 (262)

1 (10)

NAd-KER7

12 (6)

0 (0)

88 (44)

0 (0)

 

Table 1: Summary  of the quantified datasets from four KERs of the AOP. Data is categorized by both KER and radiation type. Values of dose, response measure, time since irradiation and dose rate are quoted in terms of ‘[minimum, maximum, average]’ values. ‘N/A’ denotes fields where there was no data. The final set of rows denote the percentages of dose-response data of a given KER associated with a given radiation type.

Figure 1: Quantified datasets of the four KERs in graphical form. Each row of plots represents a KER in the following order from top to bottom: Ad-KER1, NAd-KER1, NAd-KER2 and NAd-KER7. The response measure for each KER is shown along the y-axis of each plot, and from left to right the dose, time and dose rate along the x-axes respectively.

Shown in Figure 2 below is a comparison between the two dominat radiation sources; alpha-particles (green) and photon radiation (back). For each of the response measures shown in Figure 2, different symbols denote different end-points or variants of the response as measured for each KER. In the case of chromsomal aberrations (bottom-left) there is a distinct difference in the response of different chromsomal aberration types among a given radiation type e.g. for alpha-particles PCC rings (solid stars) can be 10-100 times less abundant than dicentric chromsomal types (solid circles).

While these differences and variations are embraced by the standard AOP construction, it should be questioned if the quantitative form of these variations is of use for constructing predictive models, and whether such an application is limited only to those of direct response-response relationships where the level of variation may be reduced. Even then, such response-response relationships would need to account for radiation type effects between each KE e.g. differing cell survival rates and the fraction of total DNA damage attributable to single strand breaks (SSBs), DSBs and complex/clustered damage. These are both very different between photon and alpha-particle sources (Franken et al., 2012; Nikjoo et al., 2001). This ultimately constrains any quantitative formalism of an AOP to be radiation type specific.

Figure 2: Quantified dose-response of the four KERs in graphical form. Data is focussed on the comparison between photon and alpha-particle radiation types, in addition to the response variants for each type of response. Data is evaluated for the low-dose range of 0-2 Gy for time periods following exposure < 60 minutes for Ad-KER1 (top-left), NAd-KER1 (top-right), and NAd-KER2 (bottom-left). No restriction on the time value for data points plotted for NAd-KER7 (bottom-right) has been made.

Stainforth, R. et al., (2021), Challenges in the quantificaton approach to a radiation relevant adverse outcome patway for lung cancer. Int J Radiat Biol. 97(1):85-101.

Considerations for Potential Applications of the AOP (optional)

Addressess potential applications of an AOP to support regulatory decision-making.This may include, for example, possible utility for test guideline development or refinement, development of integrated testing and assessment approaches, development of (Q)SARs / or chemical profilers to facilitate the grouping of chemicals for subsequent read-across, screening level hazard assessments or even risk assessment. More help

At present the AOP framework is not readily used to support regulatory decision-making in radiation protection practices. The goal of developing this AOP is to bring attention to the framework to the radiation community as an effective means to organize knowledge,  identify gaps  and co-ordinate research.  We have used lung cancer as the case example due to its relevance to radon risk assessment and broadly because it can be represented as a simplified targeted path with a molecular initiating event that is specific to a radiation insult.  From this AOP, more complex networks can form which are relevant to both radiation and chemical exposure scenarios. Furthermore, as  mechanistic knowledge surrounding low dose radiation exposures becomes clear, this information can be incorporated into the AOP.  By developing this AOP, we have supported the necessary efforts highlighted by the international and national radiation protection agencies such as, the United Nations Scientific Committee on the Effects of Atomic Radiation, International Commission of Radiological Protection, International Dose Effect Alliance and the Electric Power Research Institute Radiation Program to consolidate and enhance the knowledge in understanding of low dose radiation exposures from the cellular to organelle levels within the biological system.

References

List of the literature that was cited for this AOP. More help

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